Application of Facial Symmetrical Characteristic to Transfer Learning
Application of Facial Symmetrical Characteristic to Transfer Learning
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DOI：
10.1002/tee.23273
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发表时间：
2020-10
影响因子：
1
通讯作者：
Min Zou;Mengbo You;T. Akashi
中科院分区：
文献类型：
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作者：
Min Zou;Mengbo You;T. Akashi
大多数人脸检测和识别任务都是基于完整的人脸图像和相应的标签的训练。人脸的三维（3D）结构和二维（2D）外观从正面看通常是双侧对称的。然而，有时，面部的左半部分和右半部分上的照明是不均匀的。在这种情况下，人脸的对称特征可以帮助表达不同的身份信息。这是因为即使面部图像的一侧被噪声破坏，相对侧仍然可以用于特征提取。本文提出了一种仅使用半张脸自动选择较好的半张脸进行身份识别的方法。与MegaFace在野外识别数百万个身份的挑战不同，本文的重点是用较少的训练图像为少数人构建识别系统;例如，识别系统可以为研究实验室成员或家庭成员构建访问控制系统。本文提出了一种人工人脸图像构建方法和半脸训练策略，用于预训练的传统神经网络模型的迁移学习。大量的实验结果表明，该方法通过利用人脸的对称特性，提高了最先进的模型的性能。© 2020日本电气工程师协会。出版社：Wiley Periodicals LLC
Most face detection and recognition tasks are based on the training of intact facial images and corresponding labels. Both the three‐dimensional (3D) structure and two‐dimensional (2D) appearance from the frontal view of human faces are bilaterally symmetrical in general. However, sometimes, illumination on the left and right halves of faces is uneven. In such cases, the symmetrical characteristic of human faces can facilitate expressing distinct identity information. This is because even if one side of the facial image is corrupted by noise, the opposite side can still be used for feature extraction. This paper proposes an automatic selection of the better half of the face using only a half‐face for identity recognition. Unlike the MegaFace challenge of recognizing millions of identities in the wild, this paper focuses on building recognition systems for a small number of people with fewer training images; the recognition system can, for example, build access control systems for research laboratory members or family members. This paper proposes an artificial face image construction method and a half‐face training strategy for transfer learning of pretrained conventional neural network models. Extensive experimental results show that the proposed method improves the performance of state‐of‐the‐art models by utilizing the symmetrical characteristics of human faces. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.